A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health.

Journal: International journal of environmental research and public health
PMID: 39767532

Abstract

BACKGROUND: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily function, and mortality. GI health screening is crucial for preventing and managing this distress. However, accurate classification methods for GI health remain unexplored. We aimed to develop machine learning (ML) models to classify GI health status (better vs. worse) by incorporating biological aging and social determinants of health (SDOH) indicators in cancer survivors.

Authors

  • Claire J Han
    Center for Healthy Aging, Self-Management and Complex Care, College of Nursing, The Ohio State University, Columbus, OH 43210, USA.
  • Xia Ning
    Department of Biomedical Informatics, the Department of Computer Science and Engineering, and the Translational Data Analytics Institute, The Ohio State University, Columbus, OH, 43210.
  • Christin E Burd
    Departments of Molecular Genetics, Cancer Biology, and Genetics, The Ohio State University, Columbus, OH 43210, USA.
  • Fode Tounkara
    The James: Cancer Treatment and Research Center, The Ohio State University, Columbus, OH 43210, USA.
  • Matthew F Kalady
    Cleveland Clinic, Cleveland, Ohio, USA.
  • Anne M Noonan
    GI Medical Oncology Section, The James: Cancer Treatment and Research Center, The Ohio State University, Columbus, OH 43210, USA.
  • Diane Von Ah
    Center for Healthy Aging, Self-Management and Complex Care, College of Nursing, The Ohio State University, Columbus, OH 43210, USA.

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